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The Data Analytics Game: How to Predict Fraudulent Claims in the Insurance Industry Using Machine Learning?

The Data Analytics Game: How to Predict Fraudulent Claims in the Insurance Industry Using Machine Learning?
November 13, 2017

How can we use machine learning in the insurance industry? Machine learning is an evolving field of data science which focuses on algorithms, written in a way that machines are able to self-study. It uses patterns and predictions to give insights about the new data set that comes in. Machine Learning could change the dynamics of the insurance industry sector. As an essential tool for insurers, it could help in improving underwriting, pricing policies and detecting fraud.

The objective of the machine learning algorithm is to minimize error and maximize the likelihood of their prediction or occurrences in a particular event. In this way, we have to extract data from insurance industry and identify patterns to come up with the business solutions. Here, ranking model is one of the ML algorithms, which is used to identify patterns in data. It also helps to predicting the likelihood of fraudulent claims in the insurance industry by using historical claims.

The ranking model algorithm helps to redefine the underwriting (quotes and inspections) and claims (complexity and fraud) process, and develop the scoring model to categorize the risks. It has delayed the required manual intervention in the issuance process, post-bind audits, and other downstream transaction and streamlined to lower the costs. By setting the stage, we can score rank which are likely to consider hazards, exceed loss threshold and detect frauds.

Let us consider investigating the claims that will probably exceed a given threshold ‘T’. The Insurance industry has the historical claim, say ‘c1’, ‘c2’,…’cn’, with associated losses ‘L1’, ‘L2’,…..’Ln’ and we have to find the new claim ‘ci’ with associated ‘Li’ that will exceed the given threshold ‘T’. Point estimation problem was widely used to predict the ultimate loss and the error measured, on the basis of the deviation.  

The bipartite ranking system is able to create a better model than the point estimation claim, predicting the possibilities. Here, output would rank value and error would rank the miscalculations caused. Ranking error would be more accurate than the misclassification occurred. 

ranking system

Ranking model algorithm could help the modern enterprise data warehouse leverage their data and make better organizational decisions to operate efficiently. Insurance companies applying this machine learning algorithm could reduce fraud in two ways: earlier identification of the fraud and allocation of resource time on the claim and spending on valid claims. Therefore, machine learning algorithm increase customer satisfaction as the valid claims are paid faster.

Ranking model algorithm

According to PwC’s recent survey, “Insurance carriers are making an unprecedented investment in transforming their policy, billing, and claims systems and processes”. Insurers are looking for more than just up-to-date systems. They want digital and analytics platforms that can help them realize the full benefits of a core transformation. Insurance analysts could do more research on the machine learning algorithms and automate the processes for underwriting, billing, and claim systems. However, their carriers are far beyond automation and operating normal policy administration and claim handling systems. Machine learning is a wide area that insurers can explore and do more research to find relative patterns in data and able to find various business solutions.

Sources

  • Ranking model predicts who dies next in GoT- an article from The hindu, dated 24/07/2017
  • Top issues –An annual report, pwc- www.pwc.com/us/insurance
  • Point estimation Vs Rank modelling, Gary Wang , FCAS, MAAA- April 15, 2016.